AI News, Deep Learning With Python
Deep Learning With Python
Deep learning techniques are so powerful because they learn the best way to represent the problem while learning how to solve the problem.
Representation learning is perhaps the biggest differentiation between deep learning models and classical machine learning algorithm.
If I had followed the advice given to beginner developers (study discrete math, start with assembler, etc.) I would never have started developing software as a profession.
You can get started in deep learning by selecting one of the best-of-breed deep learning libraries and start developing models.
You will not understand all of the internals to begin with, but you will very quickly learn how to develop and evaluate deep learning models for a variety of machine learning problems.
The best kept secret of deep learning (and even broader machine learning) is that the applied side is quite shallow.
The platform hosts libraries such as scikit-learn the general purpose machine learning library that can be used with your deep learning models.
It is because of these benefits of the Python ecosystem that two top numerical libraries for deep learning were developed for Python, Theano and the newer TensorFlow library released by Google (and adopted recently by the Google DeepMind research group).
They are intended more for research and development teams and academics interested in developing wholly new deep learning algorithms.
The saving grace is the Keras library for deep learning, that is written in pure Python, wraps and provides a consistent agnostic interface to Theano and TensorFlow and is aimed at machine learning practitioners that are interested in creating and evaluating deep learning models.
It is a little over one year old and is clearly the best-of-breed library for getting started with deep learning because of both the speed at which you can develop models and the numerical power it is built upon.
The fastest way to get a handle on deep learning and get productive at developing models for your own machine learning problems is to practice.
Very quickly you can start to pull together this knowledge and take on larger, fuller and more complicated deep learning projects.
This approach is fast and effective for three reasons: This is the approach that you can use to rapidly get up-to-speed with applied deep learning in Python with the Keras library and start tackling your own predictive modeling problems with deep learning.
This book was designed using for you as a developer to rapidly get up to speed with applied deep learning in Python using the best-of-breed library Keras.
The goal is to get you using Keras to quickly create your first neural networks as quickly as possible, then guide you through the finer points of developing deeper models and models for computer vision and natural language problems.
teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
Plus, as you should expect of any great product on the market, every Machine Learning Mastery Ebookcomes with the surest sign of confidence: my gold-standard 100% money-back guarantee.
The industry is demanding skills in machine learning.The market wants people that can deliver results, not write academic papers.
Get Ready for Core ML 2
Core ML 2 lets you integrate a broad variety of machine learning model types into your app.
In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalized linear models.
Supported features include face tracking, face detection, landmarks, text detection, rectangle detection, barcode detection, object tracking, and image registration.
API-driven services bring intelligence to any application
Developed by AWS and Microsoft, Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.
When models are ready for deployment, developers can rely on GPU-accelerated inference platforms for the cloud, embedded device or self-driving cars, to deliver high-performance, low-latency inference for the most computationally-intensive deep neural networks.
With a single programming model for all GPU platform - from desktop to datacenter to embedded devices, developers can start development on their desktop, scale up in the cloud and deploy to their edge devices - with minimal to no code changes.
- On Tuesday, May 21, 2019
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